Background:Our aim was to investigate the predictability of postoperative meningioma resection status based on clinical features.Methods:We examined 23 clinical features to assess their effectiveness in distinguishing gross total resections (GTR) from subtotal resections (STR). We analyzed whether GTR/STR cases are better predictable if the classification is based on the Simpson grading or the postoperative operative tumor volume (POTV).Results:Using a study cohort comprising a total of 157 patients, multivariate models for the preoperative prediction of GTR/STR outcome in relation to Simpson grading and POTV were developed and subsequently compared. Including only two clinical features, our models showed a notable discriminatory power in predicting postoperative resection status. Our final model, a straightforward decision tree applicable in daily clinical practice, achieved a mean AUC of 0.885, a mean accuracy of 0.866, a mean sensitivity of 0.889, and a mean specificity of 0.772 based on independent test data.Conclusions:Such models can be a valuable tool both for surgical planning and for early planning of postoperative treatment, e.g., for additional radiotherapy/radiosurgery, potentially required in case of subtotal resections.
背景：本研究旨在探讨基于临床特征预测脑膜瘤术后切除状态的可能性。 方法：我们评估了23项临床特征对全切除（GTR）与次全切除（STR）的鉴别效能，并比较了基于Simpson分级与术后肿瘤体积（POTV）两种分类标准对GTR/STR病例的预测能力差异。 结果：通过对157例患者队列的分析，我们构建并比较了基于Simpson分级和POTV的术前GTR/STR结局预测多变量模型。仅纳入两项临床特征的模型即展现出显著的术后切除状态预测区分能力。最终建立的简易决策树模型经独立测试数据验证，平均曲线下面积达0.885，准确率0.866，灵敏度0.889，特异度0.772，适用于临床日常实践。 结论：此类预测模型可作为手术规划及术后治疗（如次全切除后可能需要补充放疗/放射外科治疗）早期决策的有价值工具。
肿瘤(癌症)患者之家